Prepare MODIS Data V2.0

This code imports, cleans, and merges MODIS product data exported using the AppEEARS web application.

Exported MODIS data for the period 2000-2016:

• MOD09A1: surf reflectance b02 b05
• MOD11A2: LST Day 1km & LST Night 1km (8 day)
• MOD13Q1: EVI 250m (16 day)
• MOD15A2H: fPAR 500m & LAI 500m (8 day)

Data was exported for 50 BAMS paper sites (.csv format, “ID”, “Category”, “LAT”, “LONG”)

Attaching package: <U+393C><U+3E31>readxl<U+393C><U+3E32>

The following object is masked from <U+393C><U+3E31>package:officer<U+393C><U+3E32>:

    read_xlsx

data.table 1.12.0  Latest news: r-datatable.com

Attaching package: <U+393C><U+3E31>data.table<U+393C><U+3E32>

The following objects are masked from <U+393C><U+3E31>package:dplyr<U+393C><U+3E32>:

    between, first, last

The following object is masked from <U+393C><U+3E31>package:purrr<U+393C><U+3E32>:

    transpose

The following objects are masked from <U+393C><U+3E31>package:lubridate<U+393C><U+3E32>:

    hour, isoweek, mday, minute, month, quarter, second, wday, week, yday, year


Attaching package: <U+393C><U+3E31>magrittr<U+393C><U+3E32>

The following object is masked from <U+393C><U+3E31>package:purrr<U+393C><U+3E32>:

    set_names

The following object is masked from <U+393C><U+3E31>package:tidyr<U+393C><U+3E32>:

    extract

9 MODIS products are downloaded in 5 .csv files:

[1] "./CH4-V2-0-MOD09A1-006-results.csv"  "./CH4-V2-0-MOD11A2-006-results.csv"  "./CH4-V2-0-MOD13Q1-006-results.csv" 
[4] "./CH4-V2-0-MOD15A2H-006-results.csv"
[[1]]

[[2]]

[[3]]

[[4]]

NA

Simplify data by removing unnecssary fields:

$vis

$lst_day

$evi

$lai

$lst_night

NA

Look at what the data quality levels are, convert good to 1, keep only 1s

$vis

$lst_day

$evi

$lai

$lst_night

NA

Make a plot of daytime LST to make sure things look OK

Create a dataframe for each product consisting of LAT, LONG, DATE and the product field (e.g. LSTD)

Create a full dataframe with each site name/latlong repeated n = length(2001-01-01 to 2014-12-31) (FLUXCOM data window). Then right_join each modis product to the site name/all dates tibble to produce a complete dataframe of modis data.

full.date <- as_tibble(rep(as_date(c(as_date("2001-01-01"):as_date("2014-12-31"))),59)) ## rep by number of sites 
names(full.date) <- "DATE"
full.date <- full.date %>% add_column(ID = rep(bams.sites$ID, each = length(full.date$DATE)/59)) %>% 
                              add_column(LAT = rep(bams.sites$LAT, each = length(full.date$DATE)/59)) %>%
                                add_column(LONG = rep(bams.sites$LONG, each = length(full.date$DATE)/59))
modis.full <- full_join(full.date, lst_d.filtered, by =c("DATE","LAT","LONG"))
modis.full <- full_join(modis.full, lst_n.filtered, by=c("DATE","LAT","LONG"))
modis.full <- full_join(modis.full, evi.filtered, by=c("DATE","LAT","LONG"))
modis.full <- full_join(modis.full, b02.filtered, by = c("DATE",'LAT',"LONG"))
modis.full <- full_join(modis.full, b05.filtered, by = c("DATE","LAT","LONG"))
modis.full <- full_join(modis.full, lai.filtered, by=c("DATE","LAT","LONG"))
modis.full <- modis.full %>% distinct(ID, DATE, .keep_all = TRUE)
str(modis.full)

Classes ‘tbl_df’, ‘tbl’ and 'data.frame':   301669 obs. of  10 variables:
 $ DATE: Date, format: "2001-01-01" "2001-01-02" "2001-01-03" "2001-01-04" ...
 $ ID  : Factor w/ 59 levels "ATNeu.csv","BCBog.csv",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ LAT : num  47.1 47.1 47.1 47.1 47.1 ...
 $ LONG: num  11.3 11.3 11.3 11.3 11.3 ...
 $ LSTD: num  NA NA NA NA NA NA NA NA NA NA ...
 $ LSTN: num  NA NA NA NA NA ...
 $ EVI : num  NA NA NA NA NA NA NA NA NA NA ...
 $ b02 : num  0.198 NA NA NA NA ...
 $ b05 : num  0.169 NA NA NA NA ...
 $ LAI : num  NA NA NA NA NA NA NA NA NA NA ...

Finished! Save as .csv

# set wd
setwd("C:/Users/gavin/Box/MODIS Data/AppEEARS Data")
write.csv(modis.full, "V2.0_MODIS.csv")